





'''simple docstring'''


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
lowerCAmelCase   :	Union[str, Any]   				=logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt''')
@dataclass
class  a_  :
     __A						=     field(
         default="cifar10"      ,		metadata={"help": "Name of a dataset from the datasets package"}				)
     __A						=     field(
         default=a_      ,		metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}				)
     __A						=     field(
         default=a_      ,		metadata={"help": "The column name of the images in the files."}				)
     __A						=     field(default=a_      ,		metadata={"help": "A folder containing the training data."}				)
     __A						=     field(default=a_      ,		metadata={"help": "A folder containing the validation data."}				)
     __A						=     field(
         default=0.15      ,		metadata={"help": "Percent to split off of train for validation."}				)
     __A						=     field(
         default=a_      ,		metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of training examples to this "
                 "value if set."
             )
         }      ,		)
     __A						=     field(
         default=a_      ,		metadata={
             "help": (
                 "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
                 "value if set."
             )
         }      ,		)





     def 				lowercase__ (					self						:   List[Any]		):





        """simple docstring"""




        lowercase_     :List[str]            =					{}
        if self.train_dir is not None:
           lowercase_     :List[str]            =					self.train_dir
        if self.validation_dir is not None:
           lowercase_     :Optional[int]            =					self.validation_dir
        lowercase_     :Union[str, Any]            =					data_files if data_files else None







@dataclass
class  a_  :
     __A						=     field(
         default=a_      ,		metadata={
             "help": (
                 "The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch."
             )
         }      ,		)
     __A						=     field(
         default=a_      ,		metadata={"help": "Pretrained config name or path if not the same as model_name_or_path"}				)
     __A						=     field(
         default=a_      ,		metadata={
             "help": (
                 "Override some existing default config settings when a model is trained from scratch. Example: "
                 "n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
             )
         }      ,		)
     __A						=     field(
         default=a_      ,		metadata={"help": "Where do you want to store the pretrained models downloaded from s3"}				)
     __A						=     field(
         default="main"      ,		metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}      ,		)
     __A						=     field(default=a_      ,		metadata={"help": "Name or path of preprocessor config."}				)
     __A						=     field(
         default=a_      ,		metadata={
             "help": (
                 "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
                 "with private models)."
             )
         }      ,		)
     __A						=     field(
         default=0.75      ,		metadata={"help": "The ratio of the number of masked tokens in the input sequence."}				)
     __A						=     field(
         default=a_      ,		metadata={"help": "Whether or not to train with normalized pixel values as target."}				)







@dataclass
class  a_  (							a_				):
     __A						=     field(
         default=1e-3      ,		metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}				)







def 				UpperCAmelCase_    (		__lowerCamelCase   :   List[str]					):
   lowercase_     :Optional[Any]            =					torch.stack([example["pixel_values"] for example in examples]					)
   return {"pixel_values": pixel_values}
def 				UpperCAmelCase_    (		):
   lowercase_     :Any            =					HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)					)
   if len(sys.argv					) == 2 and sys.argv[1].endswith(".json"					):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      lowercase_     :List[str]            =					parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
   else:
      lowercase_     :Optional[int]            =					parser.parse_args_into_dataclasses()

   # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
   # information sent is the one passed as arguments along with your Python/PyTorch versions.
   send_example_telemetry("run_mae"      ,a__      ,a__					)

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"      ,datefmt="%m/%d/%Y %H:%M:%S"      ,handlers=[logging.StreamHandler(sys.stdout					)]      ,)

   if training_args.should_log:
      # The default of training_args.log_level is passive, so we set log level at info here to have that default.
      transformers.utils.logging.set_verbosity_info()

   lowercase_     :Optional[int]            =					training_args.get_process_log_level()
   logger.setLevel(a__					)
   transformers.utils.logging.set_verbosity(a__					)
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()

   # Log on each process the small summary:
   logger.warning(
       F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
       + F'distributed training: {bool(training_args.local_rank != -1					)}, 16-bits training: {training_args.fpaa}'					)
   logger.info(F'Training/evaluation parameters {training_args}'					)

   # Detecting last checkpoint.
   lowercase_     :List[str]            =					None
   if os.path.isdir(training_args.output_dir					) and training_args.do_train and not training_args.overwrite_output_dir:
      lowercase_     :Optional[int]            =					get_last_checkpoint(training_args.output_dir					)
      if last_checkpoint is None and len(os.listdir(training_args.output_dir					)					) > 0:
         raise ValueError(
             F'Output directory ({training_args.output_dir}) already exists and is not empty. '
             "Use --overwrite_output_dir to overcome."					)
      elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
         logger.info(
             F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
             "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."					)

    # Initialize our dataset.
   lowercase_     :str            =					load_dataset(
       data_args.dataset_name      ,data_args.dataset_config_name      ,data_files=data_args.data_files      ,cache_dir=model_args.cache_dir      ,use_auth_token=True if model_args.use_auth_token else None      ,)

   # If we don't have a validation split, split off a percentage of train as validation.
   lowercase_     :List[str]            =					None if """validation""" in ds.keys() else data_args.train_val_split
   if isinstance(data_args.train_val_split      ,a__					) and data_args.train_val_split > 0.0:
      lowercase_     :int            =					ds["""train"""].train_test_split(data_args.train_val_split					)
      lowercase_     :int            =					split["""train"""]
      lowercase_     :Tuple            =					split["""test"""]

   # Load pretrained model and image processor
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   lowercase_     :Union[str, Any]            =					{
       """cache_dir""": model_args.cache_dir,
       """revision""": model_args.model_revision,
       """use_auth_token""": True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      lowercase_     :List[Any]            =					ViTMAEConfig.from_pretrained(model_args.config_name      ,**a__					)
   elif model_args.model_name_or_path:
      lowercase_     :Optional[int]            =					ViTMAEConfig.from_pretrained(model_args.model_name_or_path      ,**a__					)
   else:
      lowercase_     :Dict            =					ViTMAEConfig()
      logger.warning("You are instantiating a new config instance from scratch."					)
      if model_args.config_overrides is not None:
         logger.info(F'Overriding config: {model_args.config_overrides}'					)
         config.update_from_string(model_args.config_overrides					)
         logger.info(F'New config: {config}'					)

    # adapt config
   config.update(
       {
           "mask_ratio": model_args.mask_ratio,
           "norm_pix_loss": model_args.norm_pix_loss,
       }					)

   # create image processor
   if model_args.image_processor_name:
      lowercase_     :int            =					ViTImageProcessor.from_pretrained(model_args.image_processor_name      ,**a__					)
   elif model_args.model_name_or_path:
      lowercase_     :Union[str, Any]            =					ViTImageProcessor.from_pretrained(model_args.model_name_or_path      ,**a__					)
   else:
      lowercase_     :Optional[Any]            =					ViTImageProcessor()

   # create model
   if model_args.model_name_or_path:
      lowercase_     :Tuple            =					ViTMAEForPreTraining.from_pretrained(
          model_args.model_name_or_path      ,from_tf=bool(".ckpt" in model_args.model_name_or_path					)      ,config=a__      ,cache_dir=model_args.cache_dir      ,revision=model_args.model_revision      ,use_auth_token=True if model_args.use_auth_token else None      ,)
   else:
      logger.info("Training new model from scratch"					)
      lowercase_     :Optional[int]            =					ViTMAEForPreTraining(a__					)

   if training_args.do_train:
      lowercase_     :List[Any]            =					ds["""train"""].column_names
   else:
      lowercase_     :Union[str, Any]            =					ds["""validation"""].column_names

   if data_args.image_column_name is not None:
      lowercase_     :Optional[int]            =					data_args.image_column_name
   elif "image" in column_names:
      lowercase_     :List[Any]            =					"""image"""
   elif "img" in column_names:
      lowercase_     :Union[str, Any]            =					"""img"""
   else:
      lowercase_     :int            =					column_names[0]

   # transformations as done in original MAE paper
   # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
   if "shortest_edge" in image_processor.size:
      lowercase_     :Optional[int]            =					image_processor.size["""shortest_edge"""]
   else:
      lowercase_     :Dict            =					(image_processor.size["""height"""], image_processor.size["""width"""])
   lowercase_     :str            =					Compose(
       [
           Lambda(lambda __lowerCamelCase					: img.convert("RGB"					) if img.mode != "RGB" else img					),
           RandomResizedCrop(a__      ,scale=(0.2, 1.0)      ,interpolation=InterpolationMode.BICUBIC					),
           RandomHorizontalFlip(),
           ToTensor(),
           Normalize(mean=image_processor.image_mean      ,std=image_processor.image_std					),
       ]					)

   def preprocess_images(__lowerCamelCase   :   Dict					):
      lowercase_     :str            =					[transforms(a__					) for image in examples[image_column_name]]
      return examples

   if training_args.do_train:
      if "train" not in ds:
         raise ValueError("--do_train requires a train dataset"					)
      if data_args.max_train_samples is not None:
         lowercase_     :Optional[int]            =					ds["""train"""].shuffle(seed=training_args.seed					).select(range(data_args.max_train_samples					)					)
      # Set the training transforms
      ds["train"].set_transform(a__					)

   if training_args.do_eval:
      if "validation" not in ds:
         raise ValueError("--do_eval requires a validation dataset"					)
      if data_args.max_eval_samples is not None:
         lowercase_     :Any            =					(
             ds["""validation"""].shuffle(seed=training_args.seed					).select(range(data_args.max_eval_samples					)					)
         )
      # Set the validation transforms
      ds["validation"].set_transform(a__					)

   # Compute absolute learning rate
   lowercase_     :Optional[int]            =					(
       training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
   )
   if training_args.base_learning_rate is not None:
      lowercase_     :int            =					training_args.base_learning_rate * total_train_batch_size / 2_56

   # Initialize our trainer
   lowercase_     :Any            =					Trainer(
       model=a__      ,args=a__      ,train_dataset=ds["train"] if training_args.do_train else None      ,eval_dataset=ds["validation"] if training_args.do_eval else None      ,tokenizer=a__      ,data_collator=a__      ,)

   # Training
   if training_args.do_train:
      lowercase_     :Optional[int]            =					None
      if training_args.resume_from_checkpoint is not None:
         lowercase_     :Any            =					training_args.resume_from_checkpoint
      elif last_checkpoint is not None:
         lowercase_     :Dict            =					last_checkpoint
      lowercase_     :int            =					trainer.train(resume_from_checkpoint=a__					)
      trainer.save_model()
      trainer.log_metrics("train"      ,train_result.metrics					)
      trainer.save_metrics("train"      ,train_result.metrics					)
      trainer.save_state()

   # Evaluation
   if training_args.do_eval:
      lowercase_     :Union[str, Any]            =					trainer.evaluate()
      trainer.log_metrics("eval"      ,a__					)
      trainer.save_metrics("eval"      ,a__					)

   # Write model card and (optionally) push to hub
   lowercase_     :int            =					{
       """tasks""": """masked-auto-encoding""",
       """dataset""": data_args.dataset_name,
       """tags""": ["""masked-auto-encoding"""],
   }
   if training_args.push_to_hub:
      trainer.push_to_hub(**a__					)
   else:
      trainer.create_model_card(**a__					)
def 				UpperCAmelCase_    (		__lowerCamelCase   :   str					):
   main()


if __name__ == "__main__":
 main()





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__snake_case				:      Optional[int]   		=			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMForMultipleChoice""",
	    """XLMForQuestionAnswering""",
	    """XLMForQuestionAnsweringSimple""",
	    """XLMForSequenceClassification""",
	    """XLMForTokenClassification""",
	    """XLMModel""",
	    """XLMPreTrainedModel""",
	    """XLMWithLMHeadModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:      Optional[Any]   		=			[
	    """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMForMultipleChoice""",
	    """TFXLMForQuestionAnsweringSimple""",
	    """TFXLMForSequenceClassification""",
	    """TFXLMForTokenClassification""",
	    """TFXLMMainLayer""",
	    """TFXLMModel""",
	    """TFXLMPreTrainedModel""",
	    """TFXLMWithLMHeadModel""",
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	__snake_case				:      Union[str, Any]   		=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


